Trigger-based retraining (schedule, drift, performance) in MLOps - Step-by-Step Execution

Process Flow - Trigger-based retraining (schedule, drift, performance)

Start Monitoring

↓

Check Schedule?

Yes→Trigger Retrain

↓

Update Model

↓

Check Data Drift?

Yes→Trigger Retrain

↓

Update Model

↓

Check Performance?

Yes→Trigger Retrain

↓

Update Model

↓

Wait for Next Cycle

↩Back to Start Monitoring

The system continuously monitors schedule, data drift, and performance to decide when to retrain the model.

Execution Sample

MLOps

IF current_time == retrain_time THEN
  retrain_model()
ELSE IF data_drift_detected THEN
  retrain_model()
ELSE IF performance_below_threshold THEN
  retrain_model()
ELSE
  wait()

This pseudocode checks three triggers: schedule, data drift, and performance to decide retraining.

Process Table

Step	Current Time	Data Drift Detected	Performance Status	Action Taken	Model Retrained
1	08:00	No	Good	Wait	No
2	12:00	Yes	Good	Trigger Retrain	Yes
3	12:05	No	Good	Wait	No
4	16:00	No	Below Threshold	Trigger Retrain	Yes
5	20:00	No	Good	Wait	No
6	00:00	No	Good	Trigger Retrain (Scheduled)	Yes
7	00:05	No	Good	Wait	No

💡 Process continues indefinitely, checking triggers each cycle.

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	After Step 7
Current Time	08:00	08:00	12:00	12:05	16:00	20:00	00:00	00:05
Data Drift Detected	No	No	Yes	No	No	No	No	No
Performance Status	Good	Good	Good	Good	Below Threshold	Good	Good	Good
Model Retrained	No	No	Yes	No	Yes	No	Yes	No

Key Moments - 3 Insights

Why does the model retrain at step 2 even though the schedule time hasn't arrived?

Because data drift was detected at step 2, triggering retraining as shown in the execution_table row 2.

At step 4, why is retraining triggered despite no data drift?

Performance dropped below the threshold at step 4, which triggers retraining according to the logic in the execution_table.

Why does the model retrain at step 6 even though data drift and performance are normal?

Because the scheduled retraining time arrived at step 6, triggering retraining regardless of other conditions.

Visual Quiz - 3 Questions

Look at the execution_table, what is the action taken at step 3?

A Trigger Retrain

B Check Data Drift

C Wait

D Update Model

💡 Hint

Refer to the 'Action Taken' column in execution_table row 3.

At which step does performance status cause retraining?

A Step 2

B Step 4

C Step 6

D Step 1

💡 Hint

Check 'Performance Status' and 'Action Taken' columns in execution_table.

If data drift was detected at step 5, what would be the action?

A Trigger Retrain

B Wait

C Ignore Drift

D Schedule Retrain

💡 Hint

Look at how data drift triggers retraining in execution_table step 2.

Concept Snapshot

Trigger-based retraining checks three main triggers:
- Scheduled time to retrain
- Data drift detection
- Performance drop below threshold
If any trigger is true, retraining starts.
Otherwise, system waits and monitors again.

Full Transcript

This visual execution shows how a machine learning system decides when to retrain a model. It checks three triggers: scheduled retraining time, data drift detection, and performance status. At each step, the system evaluates these conditions. If any condition is met, it triggers retraining. For example, at step 2, data drift causes retraining. At step 4, poor performance triggers retraining. At step 6, scheduled retraining happens regardless of other conditions. Variables like current time, data drift status, and performance status change over time and influence the retraining decision. This cycle repeats continuously to keep the model updated and accurate.